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Enhancing Demand Prediction in Open Systems by Cartogram-aided Deep Learning
In this study, we propose a deep learning framework to predict rental and return patterns by leveraging cartogram approaches.
Correlation-enhanced viable core in metabolic networks
Cellular ingredient concentrations can be stabilized by adjusting generation and consumption rates through multiple pathways.
Heterogeneous popularity of metabolic reactions from evolution
Introducing an evolutionary model concretizing the stochastic recruitment of chemical reactions into the metabolism of different species at different times and their inheritance to descendants, we demonstrate that the exponential growth of the number of species containing a reaction and the saturated recruitment rate of brand-new reactions lead to the empirically identified power-law popularity distribution.
